Technical Staff Member – Agent

Responsibilities

• Design and develop new agents, proposing new research directions, e.g., combining state‑of‑the‑art RL with foundation models (LLMs/VLMs).
• Design, implement, and scale complex, high‑performance systems for training large‑scale agents. This includes both the foundational infrastructure and the novel algorithms, reward models, and sophisticated training environments.
• Collaborate closely with researchers and engineers to implement, test, and productionize new agent logics, learning algorithms, and system architectures.
• Create, manage, and scale massive benchmarks and evaluation systems to rigorously track agent capabilities. You will own system reliability, scalability, and observability for our entire research infrastructure.
• Mentor and guide other engineers and researchers on the team, fostering technical excellence. You will establish and enforce engineering standards, tooling, and best practices for both code and research design.
• Conduct thorough code and design reviews, champion technical innovation, and proactively address technical debt to accelerate the R&D lifecycle.

Requirements

• Previous demonstrable role(s) as a Staff, Principal, or Senior Engineer (or equivalent Research Scientist) in a Frontier AI Lab with a proven track record of leading complex, end‑to‑end AI/ML projects from conception to production.
• Preferably PhD (or equivalent research experience) in Machine Learning, Computer Science, or a related field, preferably with a strong publication record (e.g., NeurIPS, ICML, ICLR) in Computer Science.
• Deep theoretical and practical expertise in Agentic AI and proven experience building, scaling, and shipping solutions involving foundation models (LLMs/VLMs).
• (Bonus Skills) Practical experience applying Reinforcement Learning to systems built on Large Language Models (LLMs).
• Experience with distributed systems or cloud computing, preferably in AWS.
• Familiarity with building complex simulation environments for agent training.
• Experience with LLM training or fine‑tuning.
• Experience developing large‑scale evaluation and benchmarking systems for AI models.
• Experience in an agentic framework (e.g., LangChain, AutoGen, CrewAI, OpenAI SDK).
• Expertise in system architecture, instrumentation, observability, and monitoring for complex, high‑performance systems.

Core Competencies

Demonstrates deep expertise in Agentic AI, including the design and development of large‑scale agents and systems, with a strong focus on Reinforcement Learning and foundation models. Proven ability to mentor teams, enforce engineering standards, and manage complex AI/ML projects from conception to production.